Currently, deep learning techniques based on convolutional neural networks have made great progress in fields such as image classification, image capture and search, facial recognition, age and voice recognition etc.
Further, quality of digital images and videos has recently been improved, and a high-quality image has a higher resolution and a larger file size. However, due to the limitation by relatively small convolution kernels (typically 3×3) of a convolutional neural network, only small images can be perceived but large images cannot be “seen”, which makes it necessary to convert a high-quality image into multiple small images. The converted images have a lower resolution and each have a file size which becomes smaller therewith. Thereby, the converted images can be perceived by the convolution kernels of the convolutional neural network.
However, the converted images often need to be further compressed to be transmitted in a limited bandwidth, and a part of information of the images may be lost during the compression. In order to improve users' perception and experience, the converted low-resolution images need to be restored to the original high-resolution image for output and display to the users. However, as some information in the original image is lost in a process of down-sampling during image compression, it is difficult to recover an image which is not different from the original high-resolution image at an output terminal, which affects the users' viewing experience to some extent.